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Humans of Field Work

Florence Kondylis's picture

Enumerators play a crucial role in the success of field-based impact evaluations. Despite the central role they play in the research process, enumerators are rarely in the spotlight. We recently interviewed a few Innovations for Poverty Action (IPA) enumerators working with us on a high-frequency market survey in Rwanda in the context of a rural feeder road upgrading project.

Weekly links April 12: informal firms, politics, PACDEV, and more on field work

Kathleen Beegle's picture
  • The World Bank’s Enterprise Survey program is not just about surveying formal firms. In some countries (such as Mozambique, Zimbabwe & Lao DPR), they now also conduct surveys of informal firm. A harder-to-sample group than households or formal firms, the surveys use stratified adaptive cluster sampling methods—a method commonly used in the field of biology.
 

How to Implicit Association Test?

Florence Kondylis's picture

How to do Implicit Association Test?
Implicit Association Tests (IATs) are being increasingly used in applied micro papers. While IATs can be found off-the-shelf, designing your own IAT may allow you to get at respondents’ implicit attitudes towards something more contextual. We added a custom IAT to a survey of commuters in Rio de Janeiro, and here we'll go over the practical steps involved. For our project, we wanted to measure male and female commuters’ implicit attitudes towards women riding the subway on the co-ed car relative to women riding the women’s-only car. The idea was to quantify the stigma women may face for not using gender-segregated spaces.

Local expert opinion on prices is a good substitute for a full-fledged market price survey – since local prices are often missing this is great news

Jed Friedman's picture

The comparison of poverty rates across two countries, or across regions within a country, is a common occurrence in analysis produced at the World Bank and other development agencies, as well as in published academic papers. For any poverty comparison to have meaning, however, the analyst needs to norm the various observed states of the world to a known standard of living. In other words, any poverty comparison is meaningful only if it can be said to achieve welfare consistency.

Welfare consistent comparison across space requires local price data so that levels of living measured in dollars earned, or dollars consumed, do not get confounded with the differences in price levels across localities. After all, a poor area may be only nominally poor due to a low cost of living, but not any poorer in real terms. How would we know the difference without the right prices?

Weekly links April 5: Banerjee on UBIs in India, is Stata constraining African development? What about beer? Mentoring underrepresented women, and more...

David McKenzie's picture
  • Abhijit Banerjee discusses UBIs in the Caravan magazineon whether a UBI could be feasible in India “If you think of PROGRESA [a conditional cash-transfer scheme in Mexico], then I would say it is kind of the size of the program it would be in India. Even Brazil’s Bolsa Familia [a direct cash-transfer scheme for the poor]. These are conditional cash-transfer schemes—with eligibility conditions such as primary education—but the conditions are not incredibly onerous; they are pretty light. In fact, the Congress is talking about something that is targeted to the poor, a minimum-income guarantee, and this is something that many countries deliver effectively in different forms, including some African countries. So, it is not like we are going to be the first country to adopt an income transfer to the poor on a large scale.”....and on whether targeting could lead to social discontent “Discontent is not a bad thing. Our problem is that we have had too little discontent from the poor. Our discontent is middle-class and lower-middle-class people protesting about job quotas. I would rather have discontent about the design of social services.”
  • In the latest CSWEP newsletter – practical suggestions for mentoring underrepresented minority women in economics -  “There is no single best way to mentor women of color because no singular experience nor set of challenges exists that all women of color face. Thus, the best way is a tailored approach. To accomplish this, the first step involves discussing with the mentee about her unique pathway to her profession and any obstacles she has faced along the way. Then listen..... Potential mentors, please listen to URM women students first, ask questions second, and talk last.”....that said, concrete suggestions offered include exposure to role models of the same ethnic/racial identity (through panels, videos in classrooms, examples in classes); for non-URMs, seeking opportunities to interact and develop friendships and collaborations with URM women represents one way to signal allyship; make sure your syllabus exposes students to a broad range of authors; make clear that grad school is typically funded; and other suggestions.

Reducing intimate partner violence through edutainment

Markus Goldstein's picture
When I started working on HIV, behavior change campaigns were quite in vogue.   The idea was if you bombarded folks with enough information, maybe even made them watch a movie or two, they would get the message and change their behavior.   Then some folks got creative and thought about adding community theater or radio plays to the mix as maybe a way to get the messages across in a more entertaining way.  
 

The State of Development Journals 2019: Quality, Acceptance Rates, Review Times, and Open Science

David McKenzie's picture
This is the third year in which I have attempted to put together data on development journals that is not otherwise publicly available or easy to access (see 2017, 2018). Thanks again to all the journal editors and editorial staff who graciously shared statistics with me. 

Weekly links March 29: dynamic experimentation, making data accessible and transparent, summaries of a gazillion conference papers, assessing economic significance, and more...

David McKenzie's picture
  • Max Kasy blogs about his new work on designing multiple experiments for policy choice – “Trying to identify the best policy is different from estimating the precise impact of every individual policy: as long as we can identify the best policy, we do not care about the precise impacts of inferior policies. Yet, despite this, most experiments follow protocols that are designed to figure out the impact of every policy, even the obviously inferior ones.... The key to our proposal is staging: rather than running the experiment all at once, we propose that researchers start by running a first round of the experiment with a smaller number of participants. Based on this first round, you will be able to identify which treatments are clearly not likely to be the best. You can then go on to run another round of the experiment where you focus attention on those treatments that performed well in the first round. This way you will end up with a lot more observations to distinguish between the best performing treatments.” Sounds very cool, but it does depend on short-term outcomes being your main objects of interest.
  • Why researchers should publish their data – the J-PAL blog provides some stats on the increase in data sharing requirements and practices, and the intriguing claim that “papers in top economics and political science journals with public data and code are cited between 30-45 percent more often than papers without public data and code” – which is based on preliminary work that uses changes in journal data availability requirements to attempt to make this a causal statement.

Do I need to “recruit” a control group for my trial?

Berk Ozler's picture

An article titled “Synthetic control arms can save time and money in clinical trials” that I read last month discusses how drug trials can be faster and cheaper by using data collected from real world patients instead of recruiting a control group, hence the term “synthetic controls.”[1] Proliferation of digital data in the health sector, such as “…health data generated during routine care, including electronic health records; administrative claims data; patient-generated data from fitness trackers or home medical equipment; disease registries; and historical clinical trial data” makes such designs an increasingly feasible possibility. Combined with the fact that large amounts of time and money are spent on clinical trials, the option is attractive to researchers, drug companies, and patients awaiting new treatments alike.

The work of measuring work

Kathleen Beegle's picture

Measurement is on my mind. Partly because of the passing of Alan Krueger (credited with having a major influence on the development of empirical research – notably his influential book Myth and Measurement). But also because a couple of weeks ago, I attended an all-day brainstorming meeting on “Methods and Measurement” hosted by Global Poverty Research Lab at Northwestern University and IPA. The workshop covered a range of topics on gaps and innovations in research methods related to measurement, such as: integrating data sources and applying new methods (such as satellite data and machine learning combined with household surveys to get improved yield estimates), untangling socioeconomic complex data (such as mapping social networks), crafting measurement of concepts where we lack consensus (e.g. financial health), and bringing new tech into our survey efforts (using smartphones, physical trackers, etc.).

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